Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The self as an image: Appearance and belief in visual representations of one's own face.

Cognition·2026
Same author

Biocatalytic Asymmetric Synthesis of Diversely Substituted Dihydro-1,4-2H-Benzoxazines and Benzothiazines.

Chemistry, an Asian journal·2026
Same author

Myoclonus with Bilateral Basal Ganglia Hyperintensities in Subacute Sclerosing Panencephalitis: A Case Report.

Acta neurologica Belgica·2025
Same author

Role of RNF213 in Guiding Treatment of Moyamoya Disease with Unusual Phenotypic Presentation.

The Canadian journal of neurological sciences. Le journal canadien des sciences neurologiques·2025
Same author

Moyamoya disease presenting with transient nonfocal neurological attacks in an Indian woman carrying a previously unreported RNF213 missense variant (p.Thr554Ile).

Neurogenetics·2025
Same author

Isolated Bilateral Cerebellar Dysfunction as the Initial Manifestation of HIV Infection: A Diagnostic Challenge, Case Report, and Literature Review.

Cerebellum (London, England)·2025
Same journal

Improving Cancer Driver Gene Prediction using Biological knowledge-guided Prompts for LLM.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

Exploring Complex Genetic Mechanisms in Brain Imaging Genetics via a New Multi-task Learning Method.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

A Multi-Modal Framework for Phage-Host Interaction Prediction Using Multi-View Contrastive Learning.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

Decoding Gene-Disease Associations with Computational Methods: A Survey.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

A Competitive Coevolution-based Cancer Driver Pathway Identification Algorithm for Maximizing Coverage, Mutual Exclusivity, and Subnet Importance.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

Prediction of GO Terms Based on Partitioning PPI Networks into Highly Connected Components.

IEEE transactions on computational biology and bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Sep 11, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K

Predicting Genetic Markers for Brain Tumors Using a Composite Loss.

Arijit De, Aritro Santra, Mona Tiwari

    IEEE Transactions on Computational Biology and Bioinformatics
    |August 14, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study predicts five key glioma biomarkers (IDH, 1p/19q codeletion, ATRX, MGMT, TERT) from whole slide images using deep learning. A novel composite loss function improves prediction accuracy for brain cancer prognosis.

    More Related Videos

    On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis
    06:48

    On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

    Published on: May 31, 2020

    6.0K
    Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
    09:53

    Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

    Published on: August 16, 2020

    7.3K

    Related Experiment Videos

    Last Updated: Sep 11, 2025

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    15.8K
    On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis
    06:48

    On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

    Published on: May 31, 2020

    6.0K
    Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
    09:53

    Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

    Published on: August 16, 2020

    7.3K

    Area of Science:

    • Oncology
    • Genetics
    • Computational Biology

    Background:

    • Brain cancer, particularly gliomas, has a high mortality rate.
    • Identifying genetic mutations is crucial for glioma prognosis and treatment.
    • Five key biomarkers (IDH, 1p/19q codeletion, ATRX, MGMT, TERT) are critical for understanding glioma development.

    Purpose of the Study:

    • To develop a deep learning model for simultaneous prediction of five critical glioma genetic markers.
    • To utilize whole slide images for non-invasive biomarker identification.
    • To improve glioma patient prognosis and treatment planning through accurate biomarker prediction.

    Main Methods:

    • A deep learning approach was employed to analyze whole slide images.
    • A novel composite loss function was designed, integrating individual, pairwise, and groupwise biomarker traits.
    • Specific loss components included multi-label weighted cross-entropy, conditional probability loss, and spectral graph loss.

    Main Results:

    • The proposed deep learning model achieved state-of-the-art prediction performance for the five targeted biomarkers.
    • Ablation studies confirmed the effectiveness of the composite loss function in capturing complex biomarker relationships.
    • The method demonstrates high accuracy in predicting IDH, 1p/19q codeletion status, ATRX, MGMT, and TERT.

    Conclusions:

    • Simultaneous prediction of multiple glioma biomarkers from whole slide images is feasible using deep learning.
    • The novel composite loss function significantly enhances prediction accuracy by modeling intricate biomarker interdependencies.
    • This approach offers a promising tool for comprehensive glioma prognosis and personalized treatment strategies.